Data science is an ever-evolving industry. Ask any prominent data scientist or an industry expert and he or she would say that an analytics aspirant needs to stay constantly updated about the industry. Be it programming languages involved in analytics, the functioning and recruitment process, the tools used in advancements in the allied fields like IOT, machine learning or more, there is a lot happening here in the field of the analyst.
What is Data Science?
Data science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. In recent years, Data science has evolved like a giant, every organization, every industry is demanding data scientist.
The organizations, which are not following data science, are actually shutting down, due to losses and an increase in the competition. There are many companies that provide the course for data scientist.
Why and How Data Science is Important
In recent years, data has become the most important base, the new oil for all industries and data science is the electricity that powers the industry.
Here are some of the points, which would highlight the importance of data science:
- Industries need data to help them and make careful decisions and data science churns raw data into meaningful insights. Therefore, industries need data science.
- Data science is used by almost all industries. Some of the major sectors are healthcare, finance, banks, business, start-ups, etc. Because all these industries need data science for handling a large volume of data, this increases its importance.
- Data science is the career for tomorrow. Industries are becoming data-driven and innovations are being made every day. Industries require data scientists to assist them in making a smarter decision. To predict the information everyone requires data scientists.
- Data science is important for better marketing. Companies are using data to analyze their marketing strategies and create better advertisements. Decisions can be made by analyzing the customer’s feedback.
Functions of Data Science
Data science is a broad term and includes job roles with many different functions within the organisation
Here are the four key functions of data science
- Inference Statistics (FOR PRODUCT)
- Applied Scientist (AS PRODUCT)
- Systems Scientist (FOR OPERATIONS)
- ML Engineer (AS OPERATIONS)
The term refers to data scientist supporting a team that is building something
The term as referring to building something themselves.
The term product refers to data scientist building something that is customer-facing
The term operations refer to a backend system that is critical to running the business.
What is Required to Be a Data Scientist?
Data scientists are expected to know a lot — machine learning, computer science, statistics, mathematics, data visualization, communication, and deep learning. Within those areas, there are dozens of languages, frameworks, and technologies data scientists could learn.
Some of the must-have skillset to become a data scientist is:
- Understanding of statistics: Hypothesis testing, probability, descriptive and inferential statistics are the basic building blocks for data science. What is needed is to have an intuitive understanding of business statistics.
- Statistical Programming: R programming language is the most used language for statistics.
- Statistical Techniques and Algorithms
- Business Knowledge
- Communication
